Description
Machine learning based detection models can strengthen detection, but there remain some significant barriers to the widespread deployment of such techniques in operational detection systems. In this presentation, we identify the main challenges to overcome and we provide both methodological guidance and practical solutions to address them. The solutions we present are completely generic to be beneficial to any detection problem on any data type and are freely available in SecuML.The content of the presentation is mostly based on my PhD thesis “Expert-in-the-Loop Supervised Learning for Computer Security Detection Systems”.
Infos pratiques
Prochains exposés
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CHERIoT RTOS: An OS for Fine-Grained Memory-Safe Compartments on Low-Cost Embedded Devices
Orateur : Hugo Lefeuvre - The University of British Columbia
Embedded systems do not benefit from strong memory protection, because they are designed to minimize cost. At the same time, there is increasing pressure to connect embedded devices to the internet, where their vulnerable nature makes them routinely subject to compromise. This fundamental tension leads to the current status-quo where exploitable devices put individuals and critical infrastructure[…]-
SoSysec
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Compartmentalization
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Operating system and virtualization
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Hardware/software co-design
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Hardware architecture
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